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Subjective prior distributions for modeling longitudinal continuous outcomes with non-ignorable dropout
Susan M Paddock1, Patricia Ebener
1RAND Corporation, Santa Monica, CA 90407-2138, USA. paddock@rand.org
Statistics in Medicine
|November 18, 2008
Summary
Substance abuse treatment research faces challenges with missing data. Expert opinions on client treatment process scores significantly differ from standard assumptions used in pattern-mixture models (PMMs).
Area of Science:
- Clinical Psychology
- Biostatistics
- Substance Abuse Research
Background:
- Missing data in substance abuse treatment research, often due to early client dropout, complicates analysis.
- Pattern-mixture models (PMMs) are used to model outcomes and missing data mechanisms but rely on non-testable assumptions for parameter identification.
- Longitudinal modeling of continuous outcomes with missing data requires robust methods for parameter identification.
Purpose of the Study:
- To explore expert opinions on the rate of change in treatment process scores for clients with unidentified data.
- To compare expert-derived assumptions with standard assumptions used in PMMs for substance abuse treatment research.
- To investigate the impact of subjective prior assessments on PMM parameter identification and analysis conclusions.
Main Methods:
- Conducted expert interviews with five substance abuse treatment clinicians familiar with therapeutic communities and the Dimensions of Change Instrument.
- Collected expert opinions on the rate of change (slope) in continuous client-level treatment process scores for clients leaving treatment early.
- Analyzed the divergence between expert opinions and commonly used assumptions for PMM parameter identification.
Main Results:
- Expert opinions on the rate of change in treatment process scores varied significantly.
- These expert opinions substantially differed from the widely utilized assumptions employed to identify PMM parameters.
- Subjective prior assessments can address uncertainty in PMM parameter identification.
Conclusions:
- Expert-derived priors offer a valuable alternative to non-testable assumptions in PMMs for substance abuse treatment research.
- Incorporating expert knowledge can improve the handling of missing data and the interpretation of results in treatment studies.
- Subjective prior assessments are crucial for understanding uncertainty and its impact on conclusions in PMM analyses.
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